| import pandas as pd | |
| import random | |
| import numpy as np | |
| import pickle | |
| from os import makedirs | |
| from os.path import exists | |
| from gensim.models import Word2Vec, Doc2Vec | |
| from glove import Glove | |
| from sklearn.model_selection import KFold | |
| def _rnn_load(path, songs): | |
| data = pickle.load(open(path, 'rb')) | |
| emb_dict = {} | |
| for song in songs: | |
| emb_dict[song] = data[song] | |
| return emb_dict | |
| def __w2v_load(path, songs): | |
| wv = Word2Vec.load(path).wv | |
| emb_dict = {} | |
| for song in songs: | |
| emb_dict[song] = wv[song] | |
| return emb_dict | |
| def __g_load(path, songs): | |
| glove = Glove.load(path) | |
| emb_dict = {} | |
| for song in songs: | |
| emb_dict[song] = glove.word_vectors[glove.dictionary[song]] | |
| return emb_dict | |
| def __load_exp(path, songs): | |
| data = pickle.load(open(path, 'rb')) | |
| return data | |
| def get_embeddings(path, songs): | |
| path_arr = path.split('/') | |
| session_file = '/'.join(path_arr[:-1] + ['s' + path_arr[-1]]) | |
| user_file = path | |
| if 'experiments' in path: | |
| return __load_exp(user_file, songs), __load_exp(session_file, songs) | |
| if 'glove' in path: | |
| return __g_load(user_file, songs),__g_load(session_file, songs) | |
| if 'music2vec' in path: | |
| return __w2v_load(user_file, songs), __w2v_load(session_file, songs) | |
| if 'doc2vec' in path: | |
| return __w2v_load(user_file, songs), __w2v_load(session_file, songs) | |
| if 'rnn' in path: | |
| return _rnn_load(user_file, songs), _rnn_load(session_file, songs) | |
| return {},{} | |
| def prepare_data(df, conf): | |
| ds = conf['evaluation']['dataset'] | |
| path_kfold = 'tmp/{}/kfold/'.format(ds) | |
| if exists(path_kfold): | |
| kfold = [] | |
| for i in range(0, conf['evaluation']['k']): | |
| j = i + 1 | |
| train = pd.read_pickle(path_kfold + 'train_{}.pkl'.format(j)) | |
| test = pd.read_pickle(path_kfold + 'test_{}.pkl'.format(j)) | |
| kfold.append((train, test)) | |
| return kfold | |
| makedirs('tmp/{}/kfold/'.format(ds)) | |
| sessions = df.groupby('session')['song'].apply(lambda x: x.tolist()) | |
| users = df.groupby('user').agg(list) | |
| users['history'] = users['session'].apply(lambda x: [sessions[session] for session in list(set(x))]) | |
| users = users.drop(['song', 'timestamp','session'], axis=1) | |
| unique_users = df.user.unique() | |
| kf = KFold(n_splits=conf['evaluation']['k'], shuffle=True) | |
| i = 1 | |
| kfold = [] | |
| for train, test in kf.split(unique_users): | |
| train_df = users[users.index.isin(unique_users[train])] | |
| test_df = users[users.index.isin(unique_users[test])] | |
| train_df.to_pickle('tmp/{}/kfold/train_{}.pkl'.format(ds, i)) | |
| test_df.to_pickle('tmp/{}/kfold/test_{}.pkl'.format(ds, i)) | |
| kfold.append((train_df, test_df)) | |
| i += 1 | |
| return kfold | |